多目的進化アルゴリズムによるスマート農業マイクログリッド構成と生産エネルギー効率の協調最適化に関する研究
Research on Coordinated Optimization of Smart Agriculture Microgrid Configuration and Production Energy Efficiency Driven by Multi-objective Evolutionary Algorithm (原題)
S. T. Zhang, Sinan Xia, N. Liu
🤖 gxceed AI 要約
日本語
本研究は、スマート農業パークのマイクログリッド計画において、容量選定、系統連系、農業負荷を統合した「源-蓄-負-生産」結合モデルを提案。2024年の華北平原の実測データに基づき、炭素制約付き協調最適化により年間コスト20.89%削減、炭素排出72.18%削減、生産エネルギー効率37.45%向上を達成。多目的進化アルゴリズムPEC-MOEAが既存手法より優れた性能を示した。
English
This study proposes a 'source-storage-load-production' coupled model for smart agriculture park microgrid planning, integrating capacity selection, grid connection, and agricultural loads. Based on 2024 field data from the North China Plain, the carbon-constrained collaborative optimization reduced annualized cost by 20.89%, carbon emissions by 72.18%, and improved production energy efficiency by 37.45%. The multi-objective evolutionary algorithm PEC-MOEA outperformed five benchmark algorithms.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の農業分野では、営農型太陽光発電やスマート農業の推進が進む中、本研究成果は農業マイクログリッドの設計指針として参考になる。特に、炭素制約と生産効率を同時に考慮する枠組みは、今後の農業分野のGX推進に示唆を与える。
In the global GX context
This research contributes to the global discourse on agricultural decarbonization by demonstrating a holistic microgrid planning approach that couples energy and production systems. It provides empirical evidence from China that can inform similar initiatives in other regions, aligning with global efforts to integrate renewable energy in agriculture.
👥 読者別の含意
🔬研究者:Provides a novel coupled model and algorithm for agricultural microgrid optimization, useful for further research in energy-agriculture nexus.
🏢実務担当者:Offers a practical framework for designing cost-effective and low-carbon microgrids in agricultural parks, potentially applicable to Japanese smart agriculture projects.
🏛政策担当者:Highlights the potential of integrated energy planning in agriculture to achieve carbon reduction targets, informing policy on agricultural energy transition.
📄 Abstract（原文）
The smart agriculture park is integrating energy issues into the production process. Electricity is no longer an external input but a continuous condition that maintains the environment for crops. Existing research on microgrids mostly focuses on solving costs, carbon emissions, and reliability, while agricultural loads are often compressed into an exogenous demand curve, making it difficult to identify the transmission of supply shortages to yield and quality. This study establishes a "source-storageload-production" coupled model, placing capacity selection, grid connection boundaries, and agricultural loads in the same code; the target system also examines economic efficiency, emission constraints, supply shortage risks, and production energy efficiency. Based on the results of continuous observations in a smart agriculture park in North China Plain in 2024, the carbon constraint collaborative optimization scheme reduced the total annualized cost by 20.89% compared to the grid benchmark; the carbon emission reduction was 72.18%; unit output energy consumption decreased by 27.23%; production energy efficiency increased by 37.45%, and the critical load guarantee rate reached 99.7%. Compared to five benchmark algorithms, PEC-MOEA achieved higher HV and lower IGD, and had a higher proportion of feasible solutions. The study shows that the core of agricultural microgrid planning is not merely about increasing low-carbon installed capacity, but about integrating energy timing, production tasks, and reliability baselines into the same set of constraints.
🔗 Provenance — このレコードを発見したソース
- openalex https://www.aemjournal.org/index.php/AEM/article/view/4192first seen 2026-08-26 04:49:01
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